The identification of disease genes for complex disorders has been greatly facilitated by recent advances in structural genomics. While several unidimensional genetic, physical and transcript maps of the human genome have been instrumental in identifying single-locus disease loci, these maps lack the integration and resolution required for precise localization of transcripts involved in complex genetic diseases. We have developed a method for viewing whole chromosomes which unites mapping data from multiple experimental sources onto a single scale, thus providing higher resolution, greater statistical confidence of marker placement, and superior integration than current maps. This procedure (CompView) provides a comprehensive and seamless representation of human chromosomes, simultaneously presenting clinical, biological, and structural perspectives. CompView has been successfully implemented for human chromosome 1 to localize 5,000 unique markers and 8,000 large-insert clones with a marker density of 50 kb and a resolution of 900 kb. Here, we propose to further refine this procedure and extend it to include the entire human genome. First, we will construct 1 Mb resolution framework maps of each human chromosome with an iterative process to maximize map resolution. Second, we will integrate existing genetic, cytogenetic, radiation hybrid, expressed sequence tag, physical mapping, and sequence data with our established frameworks. Furthermore, we will continue to streamline and automate the map construction process, will explore methods for including regional and chromosome-specific mapping information, and will seek to integrate additional mapping, sequence, and functional genomic data as it becomes available. Third, we will continually assess our generated framework and integrated maps, as well as the underlying genomic data, for marker placement inconsistencies. Discrepantly mapped genomic elements will be identified and either removed or re- localized, and marker positions will be refined by comparison with identified order in large genomic sequence tracts. Fourth, we will expand an existing Internet-based user interface that provides text-based and graphical viewing options for map presentation. The Internet site will be an exhaustive and immediate data portal for all available genomic information for a specific region, marker, or transcript. This project will quickly refine localizations of over 46,000 human transcripts, creating a resource for the entire biomedical community. Completion of this project will facilitate the identification of additional loci involved in mental illnesses, especially in defining chromosomal regions and candidate genes for quantitative trait loci contributing to such complex behavioral afflictions as schizophrenia, bipolar disorder, Alzheimer's disease, alcoholism and clinical depression.